Social Interactions and the 'Digital Divide': Explaining Regional Variations in Internet Use

Ritu Agarwal, Animesh Animesh, Kislaya Prasad
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引用次数: 77

Abstract

The presence of extraordinary geographical variation in Internet use in the U.S. is widely acknowledged. Prior research suggests that individual, household, and regional differences are responsible for this disparity. We argue for an alternative explanation: that individual choice is subject to social influence, and that such peer effects are the cause of the excess variation. We test this assertion with empirical analysis of a matched data set compiled from two sources. The first is a dataset collected by the Pew Charitable Trust in June 2003 comprising of a nationwide random sample where a questionnaire is used to collect details on Internet use as well as personal characteristics such as age, income, and education of respondents. The geographic location of individuals is identified by the FIP code that represents the county where the respondent resides. From the 2000 Census we then extract a number of regional characteristics for this location (such as median household income, median age, etc.). We begin with a linear probability model of choice, and then use an instrumental variables (IV) approach to address simultaneity and other problems. In addition, we present the results from a logistic regression model. Our analysis of the data provides strong evidence of peer effects, suggesting that individual Internet use is influenced by local patterns of usage. In fact, regional differences (such as between urban and rural areas) as well as individual differences appear to matter less when peer group choices are taken into account. Our IV results suggest that the correlation between individual and group choice is not a consequence of unobserved individual or regional characteristics.
社会互动和“数字鸿沟”:解释互联网使用的地区差异
人们普遍认为,美国互联网使用的地域差异很大。先前的研究表明,个人、家庭和地区差异是造成这种差异的原因。我们提出了另一种解释:个人选择受到社会影响,而这种同伴效应是造成过度变异的原因。我们通过对来自两个来源的匹配数据集的实证分析来检验这一断言。第一个是由皮尤慈善信托基金会于2003年6月收集的数据集,其中包括全国随机抽样,其中使用问卷调查来收集调查对象的互联网使用细节以及年龄、收入、教育等个人特征。个人的地理位置由FIP代码确定,FIP代码代表被调查者所在的县。从2000年的人口普查中,我们提取了该地区的一些区域特征(如家庭收入中位数、年龄中位数等)。我们从选择的线性概率模型开始,然后使用工具变量(IV)方法来解决同时性和其他问题。此外,我们提出了一个逻辑回归模型的结果。我们对数据的分析提供了同伴效应的有力证据,表明个人的互联网使用受到当地使用模式的影响。事实上,当考虑到同伴群体的选择时,地区差异(如城市和农村地区之间)以及个体差异似乎不那么重要。我们的IV结果表明,个体和群体选择之间的相关性不是未观察到的个体或区域特征的结果。
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